
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
						raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
__a         =   logging.get_logger(__name__)

__a         =   'Hello world! cécé herlolip'




def 							a		(				snake_case__:   str		,  snake_case__:   str		,  snake_case__:   bool   ):


				'''simple docstring'''
				lowercase_	    =   FairseqRobertaModel.from_pretrained(snake_case__   )
				roberta.eval()  # disable dropout
				lowercase_	    =   roberta.model.encoder.sentence_encoder
				lowercase_	    =   XLMRobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings		,  hidden_size=roberta.cfg.model.encoder_embed_dim		,  num_hidden_layers=roberta.cfg.model.encoder_layers		,  num_attention_heads=roberta.cfg.model.encoder_attention_heads		,  intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim		,  max_position_embeddings=514		,  type_vocab_size=1		,  layer_norm_eps=1e-5		,  )
				if classification_head:
								lowercase_	    =   roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

				print('''Our RoBERTa config:'''		,  snake_case__   )

				lowercase_	    =   XLMRobertaXLForSequenceClassification(snake_case__   ) if classification_head else XLMRobertaXLForMaskedLM(snake_case__   )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				lowercase_	    =   roberta_sent_encoder.embed_tokens.weight
				lowercase_	    =   roberta_sent_encoder.embed_positions.weight
				lowercase_	    =   torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c RoBERTa doesn't use them.

				lowercase_	    =   roberta_sent_encoder.layer_norm.weight
				lowercase_	    =   roberta_sent_encoder.layer_norm.bias

				for i in range(config.num_hidden_layers   ):
								# Encoder: start of layer
								lowercase_	    =   model.roberta.encoder.layer[i]
								lowercase_	    =   roberta_sent_encoder.layers[i]

								lowercase_	    =   layer.attention
								lowercase_	    =   roberta_layer.self_attn_layer_norm.weight
								lowercase_	    =   roberta_layer.self_attn_layer_norm.bias

								# self attention
								lowercase_	    =   layer.attention.self
								assert (
								    roberta_layer.self_attn.k_proj.weight.data.shape
								    == roberta_layer.self_attn.q_proj.weight.data.shape
								    == roberta_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)   )
								)

								lowercase_	    =   roberta_layer.self_attn.q_proj.weight
								lowercase_	    =   roberta_layer.self_attn.q_proj.bias
								lowercase_	    =   roberta_layer.self_attn.k_proj.weight
								lowercase_	    =   roberta_layer.self_attn.k_proj.bias
								lowercase_	    =   roberta_layer.self_attn.v_proj.weight
								lowercase_	    =   roberta_layer.self_attn.v_proj.bias

								# self-attention output
								lowercase_	    =   layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								lowercase_	    =   roberta_layer.self_attn.out_proj.weight
								lowercase_	    =   roberta_layer.self_attn.out_proj.bias

								# this one is final layer norm
								lowercase_	    =   roberta_layer.final_layer_norm.weight
								lowercase_	    =   roberta_layer.final_layer_norm.bias

								# intermediate
								lowercase_	    =   layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								lowercase_	    =   roberta_layer.fca.weight
								lowercase_	    =   roberta_layer.fca.bias

								# output
								lowercase_	    =   layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								lowercase_	    =   roberta_layer.fca.weight
								lowercase_	    =   roberta_layer.fca.bias
								# end of layer

				if classification_head:
								lowercase_	    =   roberta.model.classification_heads['''mnli'''].dense.weight
								lowercase_	    =   roberta.model.classification_heads['''mnli'''].dense.bias
								lowercase_	    =   roberta.model.classification_heads['''mnli'''].out_proj.weight
								lowercase_	    =   roberta.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								lowercase_	    =   roberta.model.encoder.lm_head.dense.weight
								lowercase_	    =   roberta.model.encoder.lm_head.dense.bias
								lowercase_	    =   roberta.model.encoder.lm_head.layer_norm.weight
								lowercase_	    =   roberta.model.encoder.lm_head.layer_norm.bias
								lowercase_	    =   roberta.model.encoder.lm_head.weight
								lowercase_	    =   roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				lowercase_	    =   roberta.encode(snake_case__   ).unsqueeze(0   )  # batch of size 1

				lowercase_	    =   model(snake_case__   )[0]
				if classification_head:
								lowercase_	    =   roberta.model.classification_heads['''mnli'''](roberta.extract_features(snake_case__   )   )
				else:
								lowercase_	    =   roberta.model(snake_case__   )[0]
				print(our_output.shape		,  their_output.shape   )
				lowercase_	    =   torch.max(torch.abs(our_output - their_output   )   ).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''   )  # ~ 1e-7
				lowercase_	    =   torch.allclose(snake_case__		,  snake_case__		,  atol=1e-3   )
				print('''Do both models output the same tensors?'''		,  '''🔥''' if success else '''💩'''   )
				if not success:
								raise Exception('''Something went wRoNg'''   )

				pathlib.Path(snake_case__   ).mkdir(parents=snake_case__		,  exist_ok=snake_case__   )
				print(F'''Saving model to {pytorch_dump_folder_path}'''   )
				model.save_pretrained(snake_case__   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
						)
						__a         =   parser.parse_args()
						convert_xlm_roberta_xl_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)






from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip